scholarly journals Genetics of brain age suggest an overlap with common brain disorders

2018 ◽  
Author(s):  
Tobias Kaufmann ◽  
Dennis van der Meer ◽  
Nhat Trung Doan ◽  
Emanuel Schwarz ◽  
Martina J. Lund ◽  
...  
Keyword(s):  
Healthy Adult ◽  
Brain Aging ◽  
Brain Disorders ◽  
Regional Patterns ◽  
Age Gap ◽  
Brain Structure And Function ◽  
Common Risk Factors ◽  
Genetic And Environmental Factors ◽  
And Function ◽  
Brain Age

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

scholarly journals Accelerated brain aging and cerebral blood flow reduction in persons with HIV

2021 ◽  
Author(s):  
Kalen J Petersen ◽  
Nicholas Metcalf ◽  
Sarah Cooley ◽  
Dimitre Tomov ◽  
Florin Vaida ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Brain Aging ◽  
Learning Algorithm ◽  
Psychomotor Speed ◽  
Deep Learning Algorithm ◽  
Blood Flow Reduction ◽  
Brain Structure And Function ◽  
N 93 ◽  
And Function

Abstract Background Persons with HIV (PWH) are characterized by altered brain structure and function. As they attain normal lifespans, it has become crucial to understand potential interactions between HIV and aging. However, it remains unclear how brain aging varies with viral load (VL). Methods In this study, we compare MRI biomarkers amongst PWH with undetectable VL (UVL; ≤50 genomic copies/ml; n=230), PWH with detectable VL (DVL; >50 copies/ml; n=93), and HIV uninfected (HIV-) controls (n=206). To quantify gray matter cerebral blood flow (CBF), we utilized arterial spin labeling. To measure structural aging, we used a publicly available deep learning algorithm to estimate brain age from T1-weighted MRI. Cognitive performance was measured using a neuropsychological battery covering five domains. Results Associations between age and CBF varied with VL. Older PWH with DVL had reduced CBF vs. PWH with UVL (p=0.02). Structurally predicted brain aging was accelerated in PWH vs. HIV- controls regardless of VL (p<0.001). Overall, PWH had impaired learning, executive function, psychomotor speed, and language compared to HIV- controls. Structural brain aging was associated with reduced psychomotor speed (p<0.001). Conclusions Brain aging in HIV is multifaceted. CBF depends on age and current VL, and is improved by medication adherence. By contrast, structural aging is an indicator of cognitive function and reflects serostatus rather than current VL.

scholarly journals Increased Brain Age Gap Estimate (BrainAGE) in Young Adults After Premature Birth

2021 ◽  
Vol 13 ◽  
Author(s):  
Dennis M. Hedderich ◽  
Aurore Menegaux ◽  
Benita Schmitz-Koep ◽  
Rachel Nuttall ◽  
Juliana Zimmermann ◽  
...  
Keyword(s):  
Premature Birth ◽  
Brain Aging ◽  
Structural Mri ◽  
Full Term ◽  
Intracranial Volume ◽  
Age Gap ◽  
Increased Risk ◽  
Aging Rates ◽  
Brain Age ◽  
Premature Born

Recent evidence suggests increased metabolic and physiologic aging rates in premature-born adults. While the lasting consequences of premature birth on human brain development are known, its impact on brain aging remains unclear. We addressed the question of whether premature birth impacts brain age gap estimates (BrainAGE) using an accurate and robust machine-learning framework based on structural MRI in a large cohort of young premature-born adults (n = 101) and full-term (FT) controls (n = 111). Study participants are part of a geographically defined population study of premature-born individuals, which have been followed longitudinally from birth until young adulthood. We investigated the association between BrainAGE scores and perinatal variables as well as with outcomes of physical (total intracranial volume, TIV) and cognitive development (full-scale IQ, FS-IQ). We found increased BrainAGE in premature-born adults [median (interquartile range) = 1.4 (−1.3–4.7 years)] compared to full-term controls (p = 0.002, Cohen’s d = 0.443), which was associated with low Gestational age (GA), low birth weight (BW), and increased neonatal treatment intensity but not with TIV or FS-IQ. In conclusion, results demonstrate elevated BrainAGE in premature-born adults, suggesting an increased risk for accelerated brain aging in human prematurity.

scholarly journals Imaging transcriptomics of brain disorders

2021 ◽  
Author(s):  
Aurina Arnatkeviciute ◽  
Ben Fulcher ◽  
Mark Bellgrove ◽  
Alex Fornito
Keyword(s):  
Gene Expression ◽  
Brain Structure ◽  
Ex Vivo ◽  
Specific Gene ◽  
Brain Disorders ◽  
Spatiotemporal Resolution ◽  
Indirect Measures ◽  
Regional Vulnerability ◽  
Brain Structure And Function ◽  
And Function

Non-invasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function invivo and has been used extensively to map the neural changes associated with different brain disorders. However,most neuroimaging techniques have limited spatiotemporal resolution and offer only indirect measures ofunderlying pathological mechanisms. The recent development of anatomically comprehensive gene-expressionatlases has opened new opportunities for studying the transcriptional correlates of non-invasively measured neuralphenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms.Here, we overview some fundamental methods in imaging transcriptomics and outline their application tounderstanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidenceindicates that spatial variations in gene expression are linked to normative changes in brain structure during agerelatedmaturation and neurodegeneration that are in part associated with cell-specific gene expression markersof gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked totranscriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstratethat spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spreadof disease across the brain. This growing body of work supports the utility of transcriptional atlases in testinghypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimagingphenotypes.

scholarly journals Deep learning-based brain age prediction in normal aging and dementia

2021 ◽  
Author(s):  
Jeyeon Lee ◽  
Brian Burkett ◽  
Hoon-Ki Min ◽  
Matthew Senjem ◽  
Emily Lundt ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Brain Aging ◽  
Lewy Body Dementia ◽  
Normal Aging ◽  
Specific Pattern ◽  
Normal Brain ◽  
Age Gap ◽  
Brain Age ◽  
Age Prediction

Abstract Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging at respective age ranges. Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative cognitive syndromes including mild cognitive impairment, AD, frontotemporal dementia, and Lewy body dementia. Occlusion analysis, performed to facilitate interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap in dementia cohorts was highly correlated with the cognitive impairment and AD biomarker. However, regions generating brain age gaps were different for each diagnosis group of which the AD continuum showed similar patterns to normal aging in the CU.

scholarly journals FOXO3, Telomere Dynamics, and Healthy Brain Aging: A COBRE Study

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 368-368
Author(s):  
Bradley Willcox ◽  
Kamal Masaki ◽  
Richard Allsopp ◽  
Kalpana Kallianpur
Keyword(s):  
Brain Aging ◽  
Peripheral Blood Leukocyte ◽  
Telomerase Activity ◽  
Cellular Aging ◽  
Human Longevity ◽  
Telomere Shortening ◽  
Wide Range ◽  
Brain Structure And Function ◽  
Blood Leukocyte ◽  
And Function

Abstract Human longevity is linked to genetic, cellular, and other complex biological and psychosocial traits. Aging is typically accompanied by gradual brain atrophy and cognitive decline, but the mechanisms are unclear. Cellular aging, characterized by telomere shortening and altered telomerase activity, is related to mortality and brain aging. Decelerated brain aging is associated with greater peripheral blood leukocyte telomere length (LTL) and, we hypothesize, may be linked to FOXO3 genotype. We will use MRI to assess brain structure and function cross-sectionally in 100 Kuakini Honolulu Heart Program Offspring. Atrophy and disrupted functional connectivity, markers of brain aging, will be examined in relation to FOXO3 and LTL. Associations between brain structural and functional differences, FOXO3 genotype and LTL will be investigated over a wide range of ages, controlling for other biological and psychosocial factors. Results may provide insight into mechanisms influencing the rate of brain aging, and may eventually extend human healthspan.

Using neuroimaging to predict brain age: insights into typical and atypical development and risk for psychopathology

2020 ◽  
Vol 124 (2) ◽  
pp. 400-403
Author(s):  
Carmela Díaz-Arteche ◽  
Divyangana Rakesh
Keyword(s):  
Learning Algorithm ◽  
Brain Maturation ◽  
Future Research ◽  
Childhood And Adolescence ◽  
Brain Structure And Function ◽  
Atypical Development ◽  
And Function ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Childhood and adolescence are characterized by complex patterns of changes in brain structure and function. Recently, Truelove-Hill et al. (Truelove-Hill M, Erus G, Bashyam V, Varol E, Sako C, Gur RC, Gur RE, Koutsouleris N, Zhuo C, Fan Y, Wolf DH, Satterthwaite TD, Davatzikos C. J Neurosci 40: 1265–1275, 2020) used a novel machine learning algorithm to capture the subtle nuances of brain maturation during adolescence in two indices based on predicted brain age. In this article, we present an overview of the brain age prediction model used, provide further insight into the utility of this multimodal index to explore typical and atypical development, and discuss avenues for future research.

scholarly journals Age-Related Trajectories of Brain Structure–Function Coupling in Female Roller Derby Athletes

2021 ◽  
Vol 12 (1) ◽  
pp. 22
Author(s):  
Derek C. Monroe ◽  
Samantha L. DuBois ◽  
Christopher K. Rhea ◽  
Donna M. Duffy
Keyword(s):  
Brain Structure ◽  
Sport Participation ◽  
Female Athletes ◽  
Brain Aging ◽  
Roller Derby ◽  
Neurodegenerative Process ◽  
Frontoparietal Network ◽  
Age Related ◽  
Brain Structure And Function ◽  
And Function

Contact and collision sports are believed to accelerate brain aging. Postmortem studies of the human brain have implicated tau deposition in and around the perivascular space as a biomarker of an as yet poorly understood neurodegenerative process. Relatively little is known about the effects that collision sport participation has on the age-related trajectories of macroscale brain structure and function, particularly in female athletes. Diffusion MRI and resting-state functional MRI were obtained from female collision sport athletes (n = 19 roller derby (RD) players; 23–45 years old) and female control participants (n = 14; 20–49 years old) to quantify structural coupling (SC) and decoupling (SD). The novel and interesting finding is that RD athletes, but not controls, exhibited increasing SC with age in two association networks: the frontoparietal network, important for cognitive control, and default-mode network, a task-negative network (permuted p = 0.0006). Age-related increases in SC were also observed in sensorimotor networks (RD, controls) and age-related increases in SD were observed in association networks (controls) (permuted p ≤ 0.0001). These distinct patterns suggest that competing in RD results in compressed neuronal timescales in critical networks as a function of age and encourages the broader study of female athlete brains across the lifespan.

The ENIGMA Brain Injury Working Group: Approach, Challenges, and Potential Benefits

2019 ◽  
Author(s):  
Elisabeth A. Wilde ◽  
Emily L. Dennis ◽  
David F Tate
Keyword(s):  
Brain Injury ◽  
Working Group ◽  
Meta Analysis ◽  
Special Issue ◽  
Group Approach ◽  
Challenges And Opportunities ◽  
Brain Structure And Function ◽  
Potential Benefits ◽  
And Function ◽  
Neuroimaging Genetics

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium brings together researchers from around the world to try to identify the genetic underpinnings of brain structure and function, along with robust, generalizable effects of neurological and psychiatric disorders. The recently-formed ENIGMA Brain Injury working group includes 8 subgroups, based largely on injury mechanism and patient population. This introduction to the special issue summarizes the history, organization, and objectives of ENIGMA Brain Injury, and includes a discussion of strategies, challenges, opportunities and goals common across 6 of the subgroups under the umbrella of ENIGMA Brain Injury. The following articles in this special issue, including 6 articles from different subgroups, will detail the challenges and opportunities specific to each subgroup.

scholarly journals Statistical Estimation of Accelerated Biological Brain Aging After Mild Traumatic Brain Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 890-890
Author(s):  
Andrei Irimia ◽  
Jun Kim ◽  
Shania Wang ◽  
Hyung Jun Lee ◽  
Van Ngo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Brain Aging ◽  
Rate Of Change ◽  
Time Period ◽  
Weighted Magnetic Resonance Imaging ◽  
Neurological Conditions ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Age

Abstract Estimating biological brain age (BA) has the potential of identifying individuals at relatively high risk for accelerated neurodegeneration. This study compares the brain’s chronological age (CA) to its BA and reveals the BA rate of change after mild traumatic brain injury (mTBI) in an aging cohort. Using T1-weighted magnetic resonance imaging (MRI) volumes and cortical thickness, volume, surface area, and Gaussian curvature obtained using FreeSurfer software; we formulated a multivariate linear regression to determine the rate of BA increase associated with mTBI. 95 TBI patients (age in years (y): μ = 41 y, σ = 17 y; range = 18 to 83) were compared to 462 healthy controls (HCs) (age: μ = 69 y, σ = 18 y; range = 25 to 95) over a 6-month time period following mTBI. Across the initial ~6 months following injury, patients’ BAs increased by ~3.0 ± 1.2 years due to their mTBIs alone, i.e., above and beyond typical brain aging. The superior temporal and parahippocampal gyri, two structures involved in memory formation and retrieval, exhibited the fastest rates of TBI-related BA. In both hemispheres, the volume of the hippocampus decreased (left: μ=0.28%, σ=4.40%; right: μ=0.12%, σ=4.84%). These findings illustrate BA estimation techniques’ potential to identify TBI patients with accelerated neurodegeneration, whose rate is strongly associated with the risk for dementia and other aging-related neurological conditions.

Sign in / Sign up

Export Citation Format

Share Document